A newer version of the Gradio SDK is available:
6.4.0
title: Project Digital-Customer-Hub-Prototype
emoji: 🏢
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 6.2.0
app_file: app.py
pinned: false
short_description: Automating Lead Scoring & CRM Integration for Global Sales O
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
Here is a comprehensive GitHub README structure tailored to the Molex Project Engineer role. This is designed to show a hiring manager that you understand the intersection of Data Engineering, AI, and Business Value.
Project: Sales-Ops Intelligence Hub (DCH Prototype) Automating Lead Scoring & CRM Integration for Global Sales Operations
- Project Overview This is a Proof of Concept Digital Customer Hub intelligence layer focused on sales lead triaging and customer interaction understanding.
Automation: Eliminates manual data entry and lead categorization.
Lead Scoring: Implements an AI-driven priority matrix.
Data Integration: Generates structured JSON outputs ready for SAP and Salesforce ingestion.
- Technical Architecture The system is built using a modern data engineering stack:
Intelligence Engine: Hugging Face BART-Large-MNLI (Zero-Shot Classification).
UI/Interface: Gradio (for stakeholder demonstration and feedback).
Data Processing: Python (Pandas/JSON).
Environment: Google Colab.
- Implementation Details A. Intent Classification & Lead Scoring The model identifies four specific business intents without requiring a pre-labeled dataset:
Urgent RFQ (High Priority)
Sales Opportunity (High Priority)
Technical Support (Medium Priority)
General Inquiry (Low Priority)
B. Data Pipeline Logic The system performs a Clean-Score-Structure workflow:
Ingestion: Receives raw text from Sales/Customer Service logs.
AI Analysis: Calculates a confidence score for the detected intent.
Prioritization: A logic-based script assigns a "System Priority" based on the AI's confidence and the intent type.
Output: Produces a standardized JSON object to maintain Data Integrity across global systems.
- Business Impact (Projected) Efficiency: Estimated 90% reduction in time spent by sales teams on manual lead qualification.
Accuracy: Improved lead routing through a synchronized "Intelligence Engine."
Scalability: Modular Python code allows for rapid deployment as an API or cloud-based microservice (AWS/Azure).
- How to Run Open the Google Colab Notebook.
Install dependencies: pip install transformers torch gradio pandas.
Run the final cell to launch the Gradio interactive dashboard.
Input a sample customer email (e.g., "I need a technical quote for the new connector series ASAP") to see real-time lead scoring.